Artificial Intelligence (AI) is transforming the field of radiotherapy, particularly in the area of contouring – the process of delineating tumours and surrounding organs at risk (OARs) on medical images (CT, MRI or PET scans). This critical step guides treatment planning and delivery, ensuring that radiation targets cancerous tissues while sparing healthy ones.
Traditionally, the process of contouring is done manually by Clinicians using specialised imaging software. It is a complex and time-consuming activity, and open to inter-observer variability (differences in how different specialists contour the same scan). With AI, Deep learning models can automatically segment tumours and OARs on scans within minutes, providing consistent and accurate contours while still allowing expert oversight…..here’s how it works:
1. Data Training:
AI models are trained using large datasets of annotated medical images. These datasets include scans where the contours of organs, tumours, and other structures have already been manually outlined by radiologists or experts.
2. Image Pre-processing:
When a new medical scan is input into the system, the AI pre-processes the image to enhance relevant features, like identifying the boundaries between different tissues, organs, and abnormal growths (such as tumours).
3. Segmentation:
Using advanced techniques like deep learning (especially convolutional neural networks or CNNs), the AI detects and segments the scan into various regions. This means it can differentiate between normal tissue, organs, and potential tumour growth.
4. Contour Prediction:
After the segmentation, the AI draws contours (boundaries) around specific areas of interest. For example, it can outline a tumour, an organ, or a region that needs radiation therapy. The algorithm applies learned patterns from previous annotated scans to predict where contours should be placed.
5. Automatic or Assisted Refinement:
The AI can provide either fully automated contours or recommendations for refinement. Radiologists review these AI-generated contours and may adjust them if needed. The advantage of AI is that it significantly speeds up the process, reducing human error and the time it takes to create accurate contours.
6. Quality Control and Feedback:
Some AI systems incorporate quality control features, providing feedback to radiologists about the accuracy of the contours. The AI can also continue to improve as more data is fed into the system, becoming increasingly accurate over time.
What are the benefits:
- Time Efficiency: AI reduces the time it takes to generate accurate contours, which is especially beneficial in fast-paced clinical environments.
- Consistency: AI offers consistent and reproducible results, minimising variability that might come from manual contouring.
- Accuracy: AI can improve contouring accuracy by learning complex patterns and identifying subtle features that may be missed by the human eye.
What are the limitations:
- Dependence on Data Quality: The accuracy of AI contouring is highly dependent on the quality and diversity of the training data.
- Need for Expert Validation: Even though AI is powerful, human radiologists still play an essential role in reviewing and validating the results.
- Integration into Clinical Practice: Implementing AI tools demands significant changes in clinical workflows, including training for healthcare professionals and updates to existing protocols.
AI-based contouring is becoming an increasingly valuable tool in radiation therapy, helping to enhance precision and improve patient outcomes while reducing the workload on medical professionals. However, addressing its limitations is crucial, and ongoing research, quality control and validation are essential to fully realise the benefits of AI in patient care.